Search Results (669)

Background: Formin proteins are crucial regulators of actin filament assembly and elongation in eukaryotic cells, playing important roles in plant development and abiotic stress responses. However, the functional characterization of formins in Brassica rapa L. remains undiscovered. Methods: A total of 27 formin family members (BrFHs) were identified through genome-wide alignment with Arabidopsis thaliana (L.) Heynh. Results: Phylogenetic analysis classified BrFH gene family into two distinct clades, designated Group I and Group II, which exhibit divergent protein architectures. Promoter analysis revealed that BrFHs contain multiple cis-regulatory elements related to growth and development, stress responses, and phytohormone signaling. These findings suggest that BrFHs may have diversified functions. Tissue-specific expression analysis revealed that BrFHs exhibit distinct expression patterns across various tissues. Notably, BrFH15 and BrFH18 are highly expressed in flowers, displaying expression profiles similar to those of floral development genes such as AP3, AGL10 and so on. Additionally, many BrFHs show dynamic expression patterns in response to cold stresses. In particular, BrFH2, BrFH19 and BrFH27 were up-regulated, and their co-expression within the gene network suggests potential roles in regulating cold stress. Conclusions: These results clarify the functional roles of BrFHs and shed light on the molecular mechanisms underlying their regulation of tissue development and responses to cold stress in Brassica rapa. Full article

The light-trap attraction rate (LTARI) is an important metric for characterizing diel activity patterns and supports studies in insect behavioral ecology and pest management. However, conventional automatic light-trap devices often rely on lethal methods (e.g., high-voltage grids or infrared heating), causing high mortality of non-target insects and severe image obstruction due to stacking of insect bodies. These issues disturb natural populations and bias attempts to quantify LTARI. Our primary objective is to develop and evaluate a non-lethal monitoring system as a methodological basis for future LTARI research, rather than to provide head-to-head quantitative comparisons with conventional traps. To address the above limitations, we propose a live-insect monitoring instrument that integrates a wind-suction trap with a Water-Flow Dispersion and Transport Structure (WF-DTS). The non-destructive trapping–dispersion–release process limits body stacking, allows captured insects to be released, and yields a community-level post-capture survival rate of 94% under the conditions tested. Experimental results show that the prototype maintains image integrity with clearly isolated single insects and achieves a detection performance of 95.6% (mAP@0.5) using the YOLOv8s model. At the inference stage, only the standard resizing and normalization operations of YOLOv8s are applied, without additional denoising, background subtraction, or data augmentation. These observations suggest that the WF-DTS generates images that are easier to segment and classify than those from conventional devices. The high detection accuracy is largely attributable to the physical dispersion of specimens and the uniform white matte background provided by the hardware design. Overall, the system constitutes a non-lethal hardware–software platform that may reduce backend processing complexity and provide a methodological basis for more accurate LTARI estimation in future, dedicated field studies. Full article

Optical defect detection based on bright-field imaging is currently one of the most widely applied inspection techniques in wafer fabrication. However, particle defects on the surface of patterned wafers are often small in size. Under bright-field optical imaging conditions, defect signals are easily overwhelmed by complex background textures and noise, seriously affecting the detectability and positioning accuracy of defects. To address this issue, this paper proposes BWD-DETR, a detection framework tailored for wafer surface defects under bright-field imaging. Based on the RT-DETR baseline, this framework integrates a wavelet backbone, an SMFI module, and a CAS-Fusion module, achieving an AP₅₀ of 96.56% and an AP_50:95 of 54.94% in bright-field wafer defect detection, with improvements of 1.64% and 2.17% over the baseline, respectively. The proposed method can effectively enhance the detection capability for sub-micron defects on the wafer surface. Full article

The AP2/ERF superfamily is a key class of transcription factors involved in plant responses to various stresses. As an ancient species, the olive tree (Olea europaea L.) exhibits considerable stress tolerance and wide adaptability. In this study, we identified 348 AP2/ERF genes in the cultivated olive variety ‘Arbequina’ at the whole-genome level. According to protein sequence alignments and phylogenetic analyses via the Maximum Likelihood method, these genes were classified into four major families: AP2, ERF/DREB, RAV, and Soloist. The ERF/DREB family was further divided into DREB and ERF subfamilies, each encompassing six groups (A1–A6 and B1–B6), with the ERF subfamily being the largest. Members of each group exhibited relatively consistent gene structures and domain/motif compositions of their encoded proteins; however, the distribution of cis-elements and expression patterns varied. Each AP2/ERF gene contained 12 light-responsive, three MeJA-responsive, three ABA-responsive, two anaerobic induction, and one MYB binding site on average. With the threshold of p value < 0.5, control TPM > 0, and |log₂(fold change)| > 0, 50 candidate genes were simultaneously up-regulated (30) or down-regulated (20) under four stress treatments (acid–aluminum, cold, disease, and wound), among which nine showed potential protein–protein interactions. This study provides a comprehensive genomic characterization of the AP2/ERF family in olive and identifies key candidate stress-responsive genes, establishing a foundation for future functional studies on the molecular mechanisms of stress adaptation in the olive tree. Full article

X-ray imaging is the most widely used modality for fracture diagnosis in clinical practice due to its efficiency and accessibility. However, automated X-ray fracture detection faces two major challenges. First, fracture regions often contain subtle and low-contrast crack patterns, making it difficult for models to capture essential fine details. Second, fractures exhibit strong directional variability, while conventional detection frameworks have limited capacity to model rotation changes. To address these issues, we propose FDEA-Net, an enhanced detection framework tailored for fracture analysis. It integrates two lightweight improvement modules. The Fracture Detail Enhancer (FDE) strengthens high-frequency textures and fine-grained structural cues that are closely associated with fracture lines. The Rotation Aware Encoder (RAE) encodes rotation-sensitive representations, improving recognition under diverse fracture orientations. Experiments on a large-scale X-ray fracture dataset show clear performance gains, achieving an mAP50 of 0.742 and an F1-score of 0.738. These findings verify the effectiveness of combining detail enhancement with rotation-aware feature modeling. FDEA-Net provides an efficient and generalizable solution for reliable detection of subtle fractures in medical imaging. Full article

Ex situ conservation in breading centers is one of the key strategies for saving giant pandas (Ailuropoda melanoleuca). Abnormal behaviors (e.g., inappetence) are key symptoms of potential health issues (e.g., Klebsiella pneumoniae) for the captives. Therefore, monitoring their normal activity patterns could set a baseline to detect these abnormalities for implementing timely interventions. However, traditional monitoring methods are labor-intensive, which often rely on manual observations. Here, we proposed a deep learning framework, termed as DeepPanda, for automatically recognizing four essential behaviors (i.e., eating, walking, resting and drinking) of giant pandas based on videos from common surveillance cameras. Experimental results demonstrated that the DeepPanda model achieved high performance on the self-established APanda dataset, with the testing mean average precision at an IoU threshold of 0.5 (mAP@0.5) of 98.8%. This methodology provides a powerful tool for monitoring the captive giant panda’s behaviors. Full article

Detecting rice leaf diseases in real-world field environments remains challenging due to varying lesion sizes, diverse lesion morphologies, complex backgrounds, and the limited availability of high-quality annotated datasets. Existing detection models often suffer from performance degradation under these conditions, particularly when training data lack sufficient diversity and structural realism. To address these challenges, this paper proposes a Latent Diffusion Inpainting (LDI) data augmentation method and an improved lightweight detection model, LGH-YOLOv12n. Unlike conventional diffusion-based augmentation methods that generate full images or random patches, LDI performs category-aware latent inpainting, synthesizing realistic lesion patterns by jointly conditioning on background context and disease categories, thereby enhancing data diversity while preserving scene consistency. Furthermore, LGH-YOLOv12n improves upon the YOLOv12n baseline by introducing GSConv in the backbone to reduce channel redundancy and enhance lesion localization, and integrating Hierarchical Multi-head Attention (HMHA) into the neck network to better distinguish disease features from complex field backgrounds. Experimental results demonstrate that LGH-YOLOv12n achieves an F1 of 86.1% and an mAP@50 of 88.3%, outperforming the YOLOv12n model trained without data augmentation by 3.3% and 5.0%, respectively. Moreover, when trained on the LDI-augmented dataset, LGH-YOLOv12n consistently outperforms YOLOv8n, YOLOv10n, YOLOv11n, and YOLOv12n, with mAP@50 improvements of 4.6%, 5.2%, 1.9%, and 2.1%, respectively. These results indicate that the proposed LDI augmentation and LGH-YOLOv12n model provide an effective and robust solution for rice leaf disease detection in complex field environments. Full article

Background: Interindividual radiosensitivity is largely driven by genetic regulation of DNA damage recognition, repair, and cell-cycle control. TP53 and CDKN1A (p21) are key genomic markers associated with differential responses to ionizing radiation. Methods: This study investigated eight functional SNP markers across several key genes involved in DNA damage responses and cellular stress regulation, including TP53, CDKN1A/p21, APC, VEGF, XPD, and RAD51, in occupational groups exposed to chronic low-dose ionizing radiation at the Stepnogorsk Mining Chemical Combine and the Balkashinskoye uranium deposit. Genotyping was performed using PCR-based assays followed by restriction fragment length polymorphism (RFLP) analysis. Allele and genotype frequencies were compared between radiation-exposed workers and matched controls within Kazakh and Russian ethnic subgroups. Statistical differences were assessed using χ² tests, and associations with radioresistance were interpreted based on established functional characteristics of each polymorphism. Results: Four SNPs showed significant allele and genotype frequency shifts in radiation-exposed populations. The TP53 intron 3 insertion allele, TP53 intron 6 A allele, TP53 Pro72 (C) allele, and p21 codon 31 A allele were consistently enriched among exposed individuals. The strongest deviations were observed in Russian workers from Stepnogorsk (p < 0.01). These alleles are functionally associated with enhanced DNA repair efficiency, modified apoptotic responses, and strengthened cell-cycle checkpoint regulation. Conclusions: Significant enrichment of radioresistance-associated TP53 and CDKN1A (p21) variants was observed in uranium industry workers chronically exposed to low-to-moderate ionizing radiation. The observed patterns support a polygenic model of adaptive responses and emphasize the importance of genetic background in determining individual radiosensitivity under occupational exposure conditions. Full article

Glyoxalase I (GLYI) is the key regulatory enzyme in the glyoxalase pathway. This pathway enables plants to neutralize methylglyoxal (MG) using glutathione (GSH), a mechanism significant for their acclimation to environmental stress. While functionally significant, the specific functions of GLYI genes in Amaranthus palmeri remain unexplored. In this study, integrated bioinformatics and expression analysis was used to identify five GLYI genes in A. palmeri. The results indicate that ApGLYI proteins are hydrophilic and slightly acidic, localized to scaffolds 1, 11, 13, and 16 of the A. palmeri genome. Phylogenetic analysis grouped ApGLYIs with other plant GLYI proteins into three distinct clades, each exhibiting conserved motif patterns. Expression analyses demonstrate that ApGLYI genes participate in both early and late regulatory phases of MG detoxification and signaling, responding to diverse stimuli including high temperature, NaCl, osmotic stress, exogenous methylglyoxal, abscisic acid (ABA), and methyl jasmonate (MeJA). Conversely, glufosinate ammonium treatment appears to compromise this cellular detoxification system. These results offer the evolutionary trajectory and functional significance of the ApGLYI gene. They establish a foundation for subsequent studies toward managing A. palmeri infestation and using these genes to improve stress resilience in cultivated crops through breeding strategies. Full article

This paper proposes a novel motion-aware framework to address key challenges in 4D millimeter-wave radar detection for autonomous driving. While existing methods struggle with sparse point clouds and dynamic object characterization, our approach introduces three key innovations: (1) A Bird’s Eye View (BEV) fusion network incorporating velocity vector decomposition and dynamic gating mechanisms, effectively encoding motion patterns through independent XY-component convolutions; (2) a gradient-aware multi-task balancing scheme using learnable uncertainty parameters and dynamic weight normalization, resolving optimization conflicts between classification and regression tasks; and (3) a two-phase progressive training strategy combining multi-frame pre-training with sparse single-frame refinement. Evaluated on the TJ4D benchmark, our method achieves 33.25% mean Average Precision (mAP)_3D with minimal parameter overhead (1.73 M), showing particular superiority in pedestrian detection (+4.16% AP) while maintaining real-time performance (24.4 FPS on embedded platforms). Comprehensive ablation studies validate each component’s contribution, with thermal map visualization demonstrating effective motion pattern learning. This work advances robust perception under challenging conditions through principled motion modeling and efficient architecture design. Full article

Accurate detection of small objects in remote sensing imagery remains challenging due to their limited texture, sparse features, and weak contrast. To address this, an enhanced small object detection model integrating top–down and bottom–up attention mechanisms is proposed. First, we design two statistical model-constrained feature pre-extraction networks to enhance the spatial patterns of small objects before feeding them into the backbone network. Next, a top–down attention mechanism followed by an overview-then-refinement process is employed to guide region-level feature extraction. Finally, a bottom–up feature fusion strategy is utilized to integrate micro features and macro structural features in a bottom–up manner, enhancing the representational capacity of limited features for small objects. Evaluations on the AI-TOD and SODA-A datasets show that our method outperforms existing benchmark models. On the AI-TOD dataset, it improves AP and AP0.5 by 0.3% and 2.7%, respectively. More notably, on the more challenging SODA-A dataset, it achieves significant gains of 0.5% in AP and 1.4% in AP0.5. These consistent enhancements across different datasets verify the effectiveness of our method in boosting detection performance, particularly for small targets. Full article

Effective detection of wood defects is essential for maximizing wood use in a sustainable industry. However, traditional methods often struggle with complex textures and irregular shapes. This work introduces MSFE-YOLOv11-OBB, an advanced framework for oriented object detection. To tackle localization and scale challenges, we propose several key innovations: (1) a Recalibration Feature Pyramid Network (FPN) with attention modules to enhance contour accuracy, (2) a CSP-PTB module that integrates CNN-based local features with transformer-based global reasoning to create a more robust pattern representation, and (3) an LSRFAConv module designed to capture subtle structural cues, improving the detection of tiny cracks. Experimental results on an industrial dataset show that our model achieves an mAP@50 of 76.2%, improving over the baseline by 4.7% while maintaining a real-time speed of 86.99 FPS. Comparative analyses confirm superior boundary fitting and multiscale recognition capabilities. By effectively characterizing defect orientation and geometry, this framework offers an intelligent, high-precision solution for automated wood detection, significantly enhancing industrial processing efficiency and resource sustainability. Full article

Background/Objectives: Segmental duplications (SDs) are major drivers of genome evolution and structural variation in primates, particularly within acrocentric chromosomes, where rDNA arrays and duplicated sequences are densely clustered. However, the evolutionary dynamics of rDNA-linked SDs across great ape lineages have remained poorly characterized due to longstanding technical limitations in genome assembly. Here, we investigate the organization, copy number variation, and evolutionary conservation of acrocentric SDs in great apes by integrating fluorescence in situ hybridization (FISH) with comparative analyses of telomere-to-telomere (T2T) genome assemblies. Methods: Using eight human-derived fosmid probes targeting SD-enriched regions flanking rDNA arrays, we analyzed multiple individuals from chimpanzee, bonobo, gorilla, and both Bornean and Sumatran orangutans. Results: Our FISH analyses revealed extensive lineage-specific variation in SD copy number and chromosomal distribution, with pronounced heteromorphism in African great apes, particularly gorillas, and more conserved patterns in orangutans. Several SDs showed fixed duplications across species, while others exhibited high levels of polymorphism and individual-specific organization. Conclusions: Comparison with T2T assemblies confirmed consistent genomic localization for a subset of probes, whereas others displayed partial discordance, highlighting the persistent challenges in resolving highly repetitive and structurally dynamic regions even with state-of-the-art assemblies. Genome-wide analyses further revealed species-specific enrichment of SDs on rDNA-bearing chromosomes, with chimpanzees and bonobos showing higher proportions than gorillas, and contrasting patterns between the two orangutan species. Overall, our results demonstrate that rDNA-linked SDs represent highly dynamic genomic compartments that have undergone differential expansion and remodeling during great ape evolution. These regions contribute substantially to inter- and intra-species structural variation and provide a mechanistic substrate for lineage-specific genome evolution, underscoring the importance of integrating cytogenetic and T2T-based approaches to fully capture the complexity of duplicated genomic landscapes. Full article

Butterfly wing color pattern formation is a process of two-dimensional morphogenesis involving long-range lateral signaling in pupal wing tissues. We hypothesized that TRP (transient receptor potential) channels, which are multimodal sensors for various stimuli, are involved in this developmental process. Using the blue pansy butterfly Junonia orithya, we injected the TRPA1 antagonists, AM0902 and AP-18, and an agonist, JT010, into pupae and observed that the eyespot core disk area in adult wings increased and decreased in response to AM0902 and JT010, respectively, although AP-18 did not induce any change. Furthermore, the eyespot outer black ring area increased in response to AM0902, and the orange ring area increased in response to JT010. We detected TRPA1 mRNA via RT-PCR in the pupal wing tissues of this species. An antibody against the J. orithya TRPA1 extracellular site induced unique aberrant color patterns with wing vein defects. These results suggest that TRPA1 is expressed in pupal wing tissue and may integrate signaling information to determine eyespot size and structure in butterfly wings. TRPA1 likely suppresses the black core disk and the outer black ring and enhances the nonblack orange ring in eyespots during development. Full article

Oncofetal reprogramming has recently emerged as a critical concept in translational cancer research, particularly for its role in driving therapeutic resistance across a variety of malignancies. This biological process refers to a pattern of gene expression that is restricted to embryogenesis, but becomes expressed again in a subpopulation of cancer cells. These genes are typically suppressed after embryogenesis, and their aberrant re-expression in tumors endows cancer cells with stem-like properties and enhanced adaptability. The goal of this review is the following: (i) comprehensively examine the multifaceted nature of oncofetal reprogramming; (ii) elucidate its underlying molecular mechanisms, including its regulators and effectors; and (iii) evaluate its consequences for the therapeutic response in different cancer types. We comprehensively integrate the latest findings from colorectal, breast, lung, liver, and other cancers to provide a detailed understanding of how oncofetal programs interfere with tumor response to treatment. Among the candidates, YAP1 and AP-1 have emerged as central transcriptional drivers of this reprogramming process, especially in colorectal and breast cancers. We also explore the distinct expression patterns of oncofetal genes across different tumor types and how these patterns correlate with treatment outcomes and patient survival. Lastly, we propose a dual-targeting therapeutic strategy that simultaneously targets both cancer stem cells and oncofetal-reprogrammed populations as a more effective approach to overcome resistance and limit recurrence. Full article